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Digital Network Twins for Next-generation Wireless: Creation, Optimization, and Challenges

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arxiv 2410.18002 v2 pith:RA3VGELD submitted 2024-10-23 cs.NI

classification cs.NI
keywords dntsnetworkchallengesbenefitscommunicationcreationdeploymentdigital
verification ladder T0 review T1 audit T2 compute T3 formal
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Digital network twins (DNTs), by representing a physical network using a virtual model, offer significant benefits such as streamlined network development, enhanced productivity, and cost reduction for next-generation (nextG) communication infrastructure. Existing works mainly describe the deployment of DNT technologies in various service sections.The full life cycle of DNTs for telecommunication has not yet been comprehensively studied, particularly in the aspects of fine-grained creation, real-time adaptation, resource-efficient deployment, and security protection. This article presents an in-depth overview of DNTs, exploring their concrete integration into networks and communication, covering the fundamental designs, the emergent applications, and critical challenges in multiple dimensions. We also include two detailed case studies to illustrate how DNTs can be applied in real-world scenarios such as wireless traffic forecasting and edge caching. Additionally, a forward-looking vision of the research opportunities in tackling the challenges of DNTs is provided, aiming to fully maximize the benefits of DNTs in nextG networks.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks

    cs.NI 2025-02 conditional novelty 4.0 of 10

    A GRU-based predictor combined with value-decomposition multi-agent reinforcement learning improves the tradeoff between user data rates and digital twin synchronization in a simulated wireless network.

  2. Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guideline

    cs.AI 2026-06 unverdicted novelty 3.5 of 10

    LoRA continued pretraining on a small U.S. transportation corpus lifts BLEU-4 and ROUGE for Qwen2.5-7B and LLaMA-3.1-8B far above the other four models tested.

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